BRIEF RESEARCH REPORT article
Front. Hum. Neurosci.
Sec. Brain Health and Clinical Neuroscience
Volume 19 - 2025 | doi: 10.3389/fnhum.2025.1626000
This article is part of the Research TopicNeurobiological Mechanisms of Adjuvant Therapies for Personalized Stroke Rehabilitation: Towards Comprehensive RecoveryView all 8 articles
Exploring Neural Activity Changes During Motor Imagery-Based Brain-Computer Interface Training with Robotic Hand for Upper Limb Rehabilitation in Ischemic Stroke Patients: A Pilot Study
Provisionally accepted
- 1Shenzhen Longhua District Central Hospital, Shenzhen, China
- 2Shenzhen Institutes of Advanced Technology, Shenzhen, China
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Objective: This pilot study aimed to evaluate the feasibility and tolerability of motor imagery (MI)-based brain-computer interface (BCI) training with robotic hand assistance for upper limb rehabilitation, and to explore preliminary neural markers in ischemic stroke patients. Methods: Three post-stroke participants performed MI tasks combined with exoskeleton-assisted movements to facilitate rehabilitation training. Electroencephalography (EEG) signals were recorded to assess the neural correlates of MI. Functional outcomes were evaluated using standard assessment tools. Results: Our results demonstrated significant improvements in motor function across all participants. Additionally, EEG analysis revealed event-related desynchronization (ERD) in the high-alpha band power at motor cortex locations, with individual differences in both the frequency and power of neural activity. However, no significant trends in neural activity were observed across the training sessions. Conclusion: These findings suggest that MI-based BCI training, combined with robotic assistance, offer a promising approach for enhancing upper limb function in ischemic stroke patients.
Keywords: Brain-computer interface, EEG, ischemic stroke, Motor Imagery, Robotic hand, Upper limb rehabilitation
Received: 09 May 2025; Accepted: 22 Sep 2025.
Copyright: © 2025 Lu, Yang, Wu, Li, Wei, Li, Li and Huai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yaping Huai, huaiyaping@163.com
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